Age-based partitioning of individual genomic inbreeding using an exponential mixture model

Abstract

Inbreeding results from the mating of related individuals and has negative consequence because it brings together deleterious variants in one individual. Inbreeding is associated with recessive diseases and reduced production or fitness. In general, inbreeding is estimated with respect to a base population that needs to be defined. Ancestors in generations anterior to the base population are considered unrelated. We herein propose a model that estimates inbreeding relative to multiple age-based classes. Each inbreeding distribution is associated to a different time in the past: recent inbreeding generating longer homozygous stretches than more ancient. Our model is a mixture of exponential distribution implemented in a hidden Markov model framework that uses marker allele frequencies, genetic distances, genotyping error rates and the sequences of observed genotypes. Based on simulations studies, we show that the inbreeding coefficients and the age of inbreeding are correctly estimated. Mean absolute errors of estimators are low, the efficiency depending on the available information. When several inbreeding classes are simulated, the model captures them if their ages are sufficiently different. Genotyping errors or low-fold sequencing data are easily accommodated in the hidden Markov model framework. Application to real data sets illustrate that the method can reveal recent different demographic histories among populations, some of them presenting very recent bottlenecks or founder effects. The method also clearly identifies individuals resulting from extreme consanguineous matings.